The relatively low classification accuracies for all three models show that modeling the phonetic characteristics for jingju singing voice is a challenging problem. Our best results are achieved with CNNs – and GMMs perform better than DNNs. Interestingly, these results contrast with the literature where Hinton et al. [1]} describe that DNNs acoustic models largely outperform GMMs for automatic speech recognition, and Maas et al. [2]} showed that CNNs perform worse than DNNs for building speech acoustic models. First, we argue that in our case DNNs perform worse than GMMs and CNNs because a small amount of training data is available. DNNs require a lot of training data to achieve good performance and note that large amounts of training data are typically not available for most MIR tasks. And second, note the CNNs used here are specifically designed to efficiently learn timbre representations [3]} while Maas et al. [2]} used small squared filters, which proved successful
in computer vision tasks.
These results show that using CNN architectures designed for the task at hand is specially beneficial in small data scenarios.
A CNN model is used in the following experiments
